The Company
A global IT services organization operating a 24/7 multi-shift workforce across three large
campuses. With over 1,700 daily commuters, fleet efficiency and sustainability outcomes were
critical to operational success.
The Challenge
Despite significant investment in EVs, utilization remained low. Manual route planning led to
uneven trip assignment, idle vehicles, and poor charging coordination. Buffer fleets increased
costs, while weak rotation limited each vehicle’s productivity. Sustainability targets were at
risk, and fleet ROI was falling short of expectations.
The Solution
Routematic deployed its AI-powered fleet deployment engine to replace manual planning
with system-led intelligence and centralized control.
Trips were intelligently chained across campuses to maximize daily utilization and eliminate idle gaps between routes. Real-time deployment logic continuously adjusted for traffic delays, charging status, vehicle range, and shift changes—ensuring that every EV remained optimally assigned throughout the day.
Predictive optimization models leveraged live demand signals alongside historical usage patterns to forecast trip requirements with greater accuracy.
This reduced the need for excess buffer vehicles and ensured proactive charging coordination instead of reactive scheduling.
The system integrated seamlessly with existing transport workflows and city charging infrastructure, enabling uninterrupted EV availability without requiring operational overhaul.
Trips were intelligently chained across campuses to maximize daily utilization and eliminate idle gaps between routes. Real-time deployment logic continuously adjusted for traffic delays, charging status, vehicle range, and shift changes—ensuring that every EV remained optimally assigned throughout the day.
Predictive optimization models leveraged live demand signals alongside historical usage patterns to forecast trip requirements with greater accuracy.
This reduced the need for excess buffer vehicles and ensured proactive charging coordination instead of reactive scheduling.
The system integrated seamlessly with existing transport workflows and city charging infrastructure, enabling uninterrupted EV availability without requiring operational overhaul.
The Impact
The transformation was measurable within months. EV utilization increased from 54 percent
to 78 percent, surpassing sustainability targets. Daily trips per vehicle rose by 20 percent, from
4.0 to 4.8. Charging downtime dropped to zero through coordinated battery and charging
management. Overall fleet size was reduced, delivering 10 percent lower operating costs.
The organization moved from underperforming EV assets to a high-efficiency, scalable
electric fleet.